Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles

Zachary P.D. Marston, Theresa M. Cira, Erin W. Hodgson, Joseph F. Knight, Ian V. MacRae, Robert L. Koch, Silvia Rondon

Research output: Contribution to journalArticlepeer-review

Abstract

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

Original languageEnglish (US)
Pages (from-to)779-786
Number of pages8
JournalJournal of economic entomology
Volume113
Issue number2
DOIs
StatePublished - Apr 6 2020

Bibliographical note

Funding Information:
We thank all those who helped collect data in the field including: James Menger, Madelaine Bartz, Kendra Moran, Courtney Garrison Hickey, Alissa Geske, Pheylan Anderson, Claire Lotzer, Traci Eicholz, Narayan Bhagroo, Julia Stuartman, Daniela Pezinni, Obiratanea da Silva Queiroz, Rafael Carlesso Aita, Nadia Bueno, Arthur Vieira and Gregory VanNostrand, and Kimon Karelis for his invaluable help at the research site in Rosemount, MN. This project was supported by grants from the Agriculture and Food Research Initiative (competitive grant no. 2016-70006-25828) of the USDA National Institute of Food and Agriculture, the University of Minnesota MnDRIVE initiative, and the Minnesota Invasive Terrestrial Plants and Pests Center through the Minnesota Environment and Natural Resources Trust Fund.

Keywords

  • crop scouting
  • multispectral
  • reflectance
  • remote sensing
  • unmanned aerial vehicle

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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